Spectra Story

# Libraries
library(tidyverse)
library(gmRi)
library(rnaturalearth)
library(scales)
library(sf)
library(gt)
library(patchwork)
library(lmerTest)
library(emmeans)
library(merTools)
library(tidyquant)
library(ggeffects)
library(performance)
library(gtsummary)
library(gt)
library(sizeSpectra)
library(ggdist)
conflicted::conflict_prefer("select", "dplyr")
conflicted::conflict_prefer("filter", "dplyr")
conflicted::conflicts_prefer(lmerTest::lmer)

# Processing functions
source(here::here("Code/R/Processing_Functions.R"))

# ggplot theme
theme_set(
  theme_gmri(
    rect = element_rect(fill = "white", color = NA), 
    strip.text.y = element_text(angle  = 0),
    axis.text.x = element_text(size = 8),
    legend.position = "bottom"))



# vectors for factor levels
area_levels <- c("GoM", "GB", "SNE", "MAB")
area_levels_long <- c("Gulf of Maine", "Georges Bank", "Southern New England", "Mid-Atlantic Bight")
# table to join for swapping shorthand for long-hand names
area_df <- data.frame(
  area = c("Scotian Shelf", "Gulf of Maine", "Georges Bank", "Southern New England", "Mid-Atlantic Bight", "All"),
  survey_area = c("SS", "GoM", "GB", "SNE", "MAB", "Northeast Shelf"),
  area_titles = c("Scotian Shelf", "Gulf of Maine", "Georges Bank", "Southern New England", "Mid-Atlantic Bight", "Northeast Shelf"))


# Degree symbol
deg_c <- "\u00b0C"

Storycrafting - Northeast Shelf Spectra

I think we need to step back a bit and find the main story again–which is about how size structure of the NES fish community has changed over time, whether that varies by region, and what covariates are associated with those changes.
The Aug draft you developed really focused on comparing regression models to consider drivers based on the fish community size spectrum as viewed from the perspective of the Wigley species set vs. the full species set.
However, I didn’t see figures of those spectra in that draft (maybe there are somewhere else and I’ve lost track). The recent slide deck really focuses on seasonal differences in the size spectra and influence of temperature. - KMills

Starting from Square Uno

I’d like for us to start by rebuilding the story of how the size structure has changed over time and regional differences.
As such, I would like for us to look at plots of the size spectrum slope for (1) the all-species length spectrum and (2) the Wigley species biomass spectrum from annual, seasonal and regional perspectives.
I am thinking this would be two panel plots (all-spp length, Wigley-spp biomass) with 5 subpanels each (NES, GOM, GB, SNE, MAB) that are set up to show lines by seasonal and annual time steps, much like the plot on the right of slide 7 in your most recent slide deck…except with NES added and an annual line added. Keeping the significant trend lines in is helpful. This is the first step, so when you have that ready, please share it with the group. - KMills

# Data used for Wigley estimates
wigley_in <- read_csv(here::here("Data/processed/wigley_species_trawl_data.csv"))
finfish_in <- read_csv(here::here("Data/processed/finfish_trawl_data.csv"))

Aside: Biomass Fraction in Each Community

If we need to make a decision about which community we want to use, here is the different biomass totals for each:

# glimpse(wigley_in)

# Load raw
# General tidying only, removal of strata outside our study area, Spring and Fall only
# (removes inshore and rarely/inconsistently sampled strata etc.)
# shrimps removed
trawl_basic <- read_csv(here::here("Data/processed/trawl_clean_all_data.csv"))

# Biomass totals
raw_totals <- trawl_basic %>% 
  distinct(est_towdate, cruise6, station, stratum, tow, comname,  biomass_kg) %>%   pull(biomass_kg) %>% 
  sum()

wig_biomass_total <- wigley_in %>% 
  distinct(est_towdate, cruise6, station, stratum, tow, comname,  biomass_kg) %>%   pull(biomass_kg) %>% 
  sum()

ffish_biomass_total <- finfish_in %>% 
  distinct(est_towdate, cruise6, station, stratum, tow, comname,  biomass_kg) %>%   pull(biomass_kg) %>% 
  sum()


tibble(
  "Community" = c("All (including crustaceans)", "All finfish", "Wigley Species Subset"),
  "Biomass Total" = c(raw_totals, ffish_biomass_total, wig_biomass_total)) %>% 
  mutate(`% of Total` = (`Biomass Total` / raw_totals)*100) %>% 
  gt()

Community	Biomass Total	% of Total
All (including crustaceans)	3781548	100.00000
All finfish	3708046	98.05629
Wigley Species Subset	3539719	93.60501

Size Spectra Trends

Wigley Species - Bodymass Spectra

Full Finfish Community - Length Spectra

# Load the three datasets
wigley_lenspectra <- read_csv(
  here::here("Data/processed/wigley_species_length_spectra.csv"))  %>% 
  mutate(survey_area = factor(survey_area, levels = area_levels))
wigley_bmspectra <- read_csv(
  here::here("Data/processed/wigley_species_bodymass_spectra.csv")) %>% 
  mutate(survey_area = factor(survey_area, levels = area_levels))
finfish_lenspectra <- read_csv(
  here::here("Data/processed/finfish_length_spectra.csv")) %>% 
  mutate(survey_area = factor(survey_area, levels = area_levels))



# Join them together
spectra_results <- bind_rows(
   list(
     "length_spectra" = wigley_lenspectra,
    "bodymass_spectra" =  wigley_bmspectra), 
   .id = "metric") %>% 
  mutate(community = "Wigley Subset") %>% 
  bind_rows(mutate(
    finfish_lenspectra,
    metric = "length_spectra",
    community = "Finfish Community")) %>% 
  mutate(
    survey_area = fct_relevel(survey_area, area_levels),
    metric = fct_rev(metric))



# Left side:
# All species  species length spectrum 
# color = season vs. annual
# facet_wrap(~survey_area)

# Right side:
# Wigley species biomass spectrum
# color = season vs. annual
# facet_wrap(~survey_area)


# Model significant trends separately
# Year as RE
lspectra_mod_ffish <- lmer(
  formula = b ~ est_year * survey_area * season + (1|est_year),
  data = finfish_lenspectra)
lspectra_mod_wig <- lmer(
  formula = b ~ est_year * survey_area * season + (1|est_year),
  data = wigley_lenspectra)
bmspectra_mod_wig <- lmer(
  formula = b ~ est_year * survey_area * season + (1|est_year),
  data = wigley_bmspectra)



# Function to get the Predictions
# Drop effect fits that are non-significant  ###
get_preds_and_trendsignif <- function(mod_x){
  modx_preds <- as.data.frame(
    # Model predictions
    ggpredict(
      mod_x, 
      terms = ~ est_year * survey_area * season) ) %>% 
    mutate(
      survey_area = factor(group, levels = area_levels),
      season = factor(facet, levels = c("Spring", "Fall")))
  
    # Just survey area and year
    modx_emtrend <- emtrends(
      object = mod_x,
      specs =  ~ survey_area*season,
      var = "est_year") %>% 
      as_tibble() %>% 
      mutate(
        zero = 0,
        non_zero = if_else(between(zero, lower.CL, upper.CL), F, T))
  
    # Preds with signif
    modx_preds %>% left_join(select(modx_emtrend, survey_area, season, non_zero))
  
}



# Get the predictions and flag whether they are significant
trend_predictions <- bind_rows(list(
  "length_spectra-Finfish Community" = get_preds_and_trendsignif(lspectra_mod_ffish),
  "length_spectra-Wigley Subset" = get_preds_and_trendsignif(lspectra_mod_wig),
  "bodymass_spectra-Wigley Subset" = get_preds_and_trendsignif(bmspectra_mod_wig)
  ), .id = "model_id") %>% 
  separate(model_id, into = c("metric", "community"), sep = "-") %>% 
  mutate(metric = fct_rev(metric))





# Plot over observed data
# Contrast seasonal differences




# Make the plot
spectra_trends_1g <- ggplot() +
   geom_ribbon(
    data = filter(trend_predictions, non_zero == TRUE),
    aes(x, ymin = conf.low, ymax = conf.high, fill = season),
    linewidth = 1, alpha = 0.35) +
  geom_point(
    data = spectra_results,
    aes(est_year, b, color = season),
    size = 0.8, alpha = 0.5) +
  geom_line(
    data = filter(trend_predictions, non_zero == TRUE),
    aes(x, predicted, color = season),
    linewidth = 1, alpha = 1) +
  scale_fill_gmri() +
  scale_color_gmri() +
  facet_grid(survey_area ~ metric*community, scales = "free") +
  labs(
    x = "Year",
    y = "Exponent of Size Spectra")
 

spectra_trends_1g

Size Spectra Summary Tables

Large Community Length Spectra

# Summary tables
lspectra_mod_ffish %>% 
  gtsummary::tbl_regression()   %>% 
  add_global_p(keep = T) %>% 
  bold_p(t = 0.05)  %>%
  bold_labels() %>%
  italicize_levels() %>% 
  as_gt() %>% 
  tab_header(title = "Full Finfish Community - Length Spectra Trends")

Full Finfish Community - Length Spectra Trends
Characteristic	Beta	95% CI 1	p-value
est_year	0.00	0.00, 0.00	0.2
survey_area			0.005
GoM	—	—
GB	1.4	-5.5, 8.4	0.7
SNE	6.8	-0.24, 14	0.058
MAB	-6.1	-13, 0.91	0.087
season			0.2
Fall	—	—
Spring	4.1	-2.9, 11	0.2
est_year * survey_area			0.005
est_year * GB	0.00	0.00, 0.00	0.6
est_year * SNE	0.00	-0.01, 0.00	0.040
est_year * MAB	0.00	0.00, 0.01	0.12
est_year * season			0.2
est_year * Spring	0.00	-0.01, 0.00	0.2
survey_area * season			0.7
GB * Spring	-1.2	-11, 8.7	0.8
SNE * Spring	-5.2	-15, 4.7	0.3
MAB * Spring	-4.5	-14, 5.5	0.4
est_year * survey_area * season			0.6
est_year * GB * Spring	0.00	0.00, 0.01	0.8
est_year * SNE * Spring	0.00	0.00, 0.01	0.3
est_year * MAB * Spring	0.00	0.00, 0.01	0.3
1 CI = Confidence Interval

Wigley Community Length Spectra

# Summary tables
lspectra_mod_wig %>% 
  gtsummary::tbl_regression()   %>% 
  add_global_p(keep = T) %>% 
  bold_p(t = 0.05)  %>%
  bold_labels() %>%
  italicize_levels() %>% 
  as_gt() %>% 
  tab_header(title = "Wigley Community - Length Spectra Trends")

Wigley Community - Length Spectra Trends
Characteristic	Beta	95% CI 1	p-value
est_year	0.00	-0.01, 0.00	0.030
survey_area			0.3
GoM	—	—
GB	-3.5	-10, 3.6	0.3
SNE	-6.4	-14, 0.69	0.077
MAB	-5.9	-13, 1.2	0.10
season			0.4
Fall	—	—
Spring	2.8	-4.2, 9.8	0.4
est_year * survey_area			0.3
est_year * GB	0.00	0.00, 0.01	0.3
est_year * SNE	0.00	0.00, 0.01	0.094
est_year * MAB	0.00	0.00, 0.01	0.13
est_year * season			0.4
est_year * Spring	0.00	0.00, 0.00	0.4
survey_area * season			0.002
GB * Spring	3.7	-6.3, 14	0.5
SNE * Spring	6.8	-3.2, 17	0.2
MAB * Spring	-12	-22, -1.6	0.023
est_year * survey_area * season			0.002
est_year * GB * Spring	0.00	-0.01, 0.00	0.5
est_year * SNE * Spring	0.00	-0.01, 0.00	0.2
est_year * MAB * Spring	0.01	0.00, 0.01	0.018
1 CI = Confidence Interval

Wigley Community Bodymass Spectra

# Summary tables
bmspectra_mod_wig %>% 
  gtsummary::tbl_regression()   %>% 
  add_global_p(keep = T) %>% 
  bold_p(t = 0.05)  %>%
  bold_labels() %>%
  italicize_levels() %>% 
  as_gt() %>% 
  tab_header(title = "Wigley Community - Mass Spectra Trends")

Wigley Community - Mass Spectra Trends
Characteristic	Beta	95% CI 1	p-value
est_year	0.00	0.00, 0.00	0.020
survey_area			0.3
GoM	—	—
GB	-2.8	-6.2, 0.71	0.12
SNE	-2.6	-6.1, 0.93	0.15
MAB	-2.6	-6.1, 0.93	0.15
season			0.5
Fall	—	—
Spring	1.2	-2.3, 4.7	0.5
est_year * survey_area			0.4
est_year * GB	0.00	0.00, 0.00	0.12
est_year * SNE	0.00	0.00, 0.00	0.2
est_year * MAB	0.00	0.00, 0.00	0.2
est_year * season			0.5
est_year * Spring	0.00	0.00, 0.00	0.5
survey_area * season			<0.001
GB * Spring	1.7	-3.2, 6.6	0.5
SNE * Spring	1.3	-3.6, 6.2	0.6
MAB * Spring	-7.3	-12, -2.4	0.004
est_year * survey_area * season			<0.001
est_year * GB * Spring	0.00	0.00, 0.00	0.5
est_year * SNE * Spring	0.00	0.00, 0.00	0.7
est_year * MAB * Spring	0.00	0.00, 0.01	0.003
1 CI = Confidence Interval

Median Length & Weight Trends

I also think it would be valuable to build out the story of size change further, so am thinking that plots like those of average (or median) length (all species) and weight (wigley species) for NES and the sub-regions, like those on p. 12 of the attached file above (the very first draft from late 2022) would be useful. - KMills

# Load the median weight/length data
wigley_size_df <- read_csv(here::here("Data/processed/wigley_species_size_summary.csv"))
finfish_size_df <- read_csv(here::here("Data/processed/finfish_species_length_summary.csv"))


# Join them
size_results <- bind_rows(
  list(
    "Finfish Community" = finfish_size_df,
    "Wigley Subset" = wigley_size_df), 
  .id = "community"
)




# Get trends:
len_mod_ffish <- lmer(
  formula = med_len_cm ~ est_year * survey_area * season + (1|est_year),
  data = finfish_size_df)
len_mod_wig <- lmer(
  formula = med_len_cm ~ est_year * survey_area * season + (1|est_year),
  data = wigley_size_df)
wt_mod_wig <- lmer(
  formula = med_wt_kg ~ est_year * survey_area * season + (1|est_year),
  data = drop_na(wigley_size_df, med_wt_kg))



# Get the predictions and flag whether they are significant
size_trend_predictions <- bind_rows(list(
  "med_len_cm-Finfish Community" = get_preds_and_trendsignif(len_mod_ffish),
  "med_len_cm-Wigley Subset"     = get_preds_and_trendsignif(len_mod_wig),
  "med_wt_kg-Wigley Subset"   = get_preds_and_trendsignif(wt_mod_wig)
  ), .id = "model_id") %>% 
  separate(model_id, into = c("metric", "community"), sep = "-") %>% 
  mutate(metric = fct_rev(metric))






# Left side: 
# Median length - all species
# color = season vs. annual
# facet_wrap(~survey_area)


size_long <- size_results %>% 
  pivot_longer(cols = c(med_len_cm, med_wt_kg), 
               names_to = "metric", 
               values_to = "val")  %>% 
  mutate(survey_area = fct_relevel(survey_area, area_levels),
         season = fct_rev(season))


# just length for scales
len_plot <-  size_long %>% 
  filter(metric == "med_len_cm") %>% 
  drop_na(val) %>% 
  ggplot() +
  geom_ribbon(
    data = filter(size_trend_predictions, metric == "med_len_cm", non_zero == TRUE),
    aes(x, ymin = conf.low, ymax = conf.high, fill = season),
    linewidth = 1, alpha = 0.35) +
  geom_point(
    #data = filter(size_long, metric == "med_len_cm"),
    aes(est_year, val, color = season),
    size = 0.8, alpha = 0.5) +
  geom_line(
    data = filter(size_trend_predictions, metric == "med_len_cm", non_zero == TRUE),
    aes(x, predicted, color = season),
    linewidth = 1, alpha = 1) +
  scale_fill_gmri() +
  scale_color_gmri() +
  facet_grid(survey_area ~ metric*community,
             scales = "free") +
  theme(strip.text.y = element_blank()) +
  labs(
    x = "Year",
    y = "Length (cm)")

# Right: 
# Median weight - wigley species
# color = season vs. annual
# facet_wrap(~survey_area)

# weight plots
wt_plot <- size_long %>% 
  filter(metric == "med_wt_kg") %>% 
  drop_na(val) %>% 
  ggplot() +
  geom_ribbon(
    data = filter(size_trend_predictions, metric == "med_wt_kg", non_zero == TRUE),
    aes(x, ymin = conf.low, ymax = conf.high, fill = season),
    linewidth = 1, alpha = 0.35) +
  geom_point(
    #data = filter(size_long, metric == "med_len_cm"),
    aes(est_year, val, color = season),
    size = 0.8, alpha = 0.5) +
  geom_line(
    data = filter(size_trend_predictions, metric == "med_wt_kg", non_zero == TRUE),
    aes(x, predicted, color = season),
    linewidth = 1, alpha = 1) +
  scale_fill_gmri() +
  scale_color_gmri() +
  facet_grid(survey_area ~ metric*community,
             scales = "free")  +
  labs(
    x = "Year",
    y = "Weight (kg)")

(len_plot | wt_plot) + plot_layout(guides = "collect", widths = c(3,1.5)) & theme(legend.position = "bottom")

A couple things have started to catch my eye the more I’ve thought about volatility in these numbers.

There seem to be two situations happening on occassion. The first is more common in GOM+GB, and that is a 5-10cm drop in median length. This sudden drop in size I am imagining is likely due to surges in new recruits.

The other patttern which is more common in MAB but looks like it happens a handful of times in GB is the reverse situation, where median size surges upward in isolated years. For these situations I think we likely could trace these down to exceptionally high catches of larger species like elasmobranchs.

Large Fish Index

NOTES: Set cutoffs using biomass. Do region * seasonally, connect moving average

When I looked at large/small fish indices I looked at two metrics:

• The large fish index (proportion of yearly biomass totals represented by fishes in top 5-10% of body sizes)

We will focus on the former for the plots below. Fish size for the below plots will be based on length because these are measured for all individuals.

Large fish will be considered anything over 55cm, representing the 90th percentile. Values are the fraction of total bodymass for a given season that is coming from the large fishes.

The fraction of the biomass held in fishes larger than 55cm is lower in the two northern areas, and falling across both seasons. In SNE its declining in the spring, and in the MAB its increasing.

##### Large Fish Index  ####


# Get 95th percentile as threshold
DescTools::Quantile(
  wigley_in$length_cm, 
  weights = wigley_in$numlen_adj, 
  probs = c(0.05, 0.1, 0.5, 0.90, .95))

 5% 10% 50% 90% 95% 
  8  10  21  55  73 

wigley_in <- wigley_in %>% 
  mutate(
    large = ifelse(length_cm > 55, T, F),
    small = ifelse(length_cm < 10, T, F))



# Get total bio
total_bio <-  wigley_in %>% 
  group_by(survey_area, est_year, season) %>% 
  summarise(
    total_bio = sum(sum_weight_kg),
    .groups = "drop")
  
  
# Get large fish bio
lf_bio <- wigley_in %>% 
  group_by(survey_area, est_year, season) %>% 
  summarise(
    small_bio = sum(sum_weight_kg * small),
    large_bio = sum(sum_weight_kg * large),
    .groups = "drop")

# Join and divide
lfi <- left_join(total_bio, lf_bio) %>% 
  mutate(LFI = large_bio / total_bio,
         SFI = small_bio / total_bio)  %>% 
  mutate(survey_area = factor(survey_area, levels = area_levels))

# trendz
# Get trends for large and small fish index
lfi_mod <- lmer(
  formula = LFI ~ est_year * survey_area * season + (1|est_year),
  data = lfi)
sfi_mod <- lmer(
  formula = SFI ~ est_year * survey_area * season + (1|est_year),
  data = lfi)

# Significant trends
lfi_predictions <- get_preds_and_trendsignif(lfi_mod) %>% 
  mutate(survey_area = factor(survey_area, levels = area_levels))
sfi_predictions <-  get_preds_and_trendsignif(sfi_mod) %>% 
  mutate(survey_area = factor(survey_area, levels = area_levels))




# Plot the SFI
sfi_p <- ggplot() +
  geom_ribbon(
    data = filter(sfi_predictions, non_zero == TRUE),
    aes(x, ymin = conf.low, ymax = conf.high, fill = season),
    linewidth = 1, alpha = 0.35) +

  geom_point(
    data = lfi, aes(est_year, SFI, color = season),
    size = 0.8, alpha = 0.5) +
    geom_line(
    data = filter(sfi_predictions, non_zero == TRUE),
    aes(x, predicted, color = season),
    linewidth = 1, alpha = 1) +
  scale_fill_gmri() +
  scale_color_gmri() +
  scale_y_continuous(
    # limits = c(0,1), 
    labels = label_percent()) +
  facet_grid(survey_area~., scales = "free") +
  labs(
    x = "Year",
    y = "Percentage of Total Biomass",
    title = "Small Fish Index (<10 cm)",
    fill = "Season",
    color = "Season")


# Plot the LFI
lfi_p <- ggplot() +
  geom_ribbon(
    data = filter(lfi_predictions, non_zero == TRUE),
    aes(x, ymin = conf.low, ymax = conf.high, fill = season),
    linewidth = 1, alpha = 0.35) +

  geom_point(
    data = lfi, aes(est_year, LFI, color = season),
    size = 0.8, alpha = 0.5) +
    geom_line(
    data = filter(lfi_predictions, non_zero == TRUE),
    aes(x, predicted, color = season),
    linewidth = 1, alpha = 1) +
  scale_fill_gmri() +
  scale_color_gmri() +
  scale_y_continuous(
    limits = c(0,1), 
    labels = label_percent()) +
  facet_grid(survey_area~., scales = "free") +
  labs(
    x = "Year",
    y = "Percentage of Total Biomass",
    title = "Large Fish Index (>55cm)",
    fill = "Season",
    color = "Season")




sfi_p | lfi_p

The following species are those which has representation in the large fish index:

big_fishes <- wigley_in %>% 
  filter(large) %>% 
  distinct(comname) %>% 
  arrange(comname) %>% 
  rename(`Common Name:` = comname) 

# big_fishes %>% 
#   gt() %>%
#   tab_header(title = "Large Fishes")


library(pander)
pander(big_fishes$`Common Name:`)

american plaice, american shad, atlantic angel shark, atlantic cod, atlantic croaker, atlantic halibut, atlantic sharpnose shark, atlantic sturgeon, atlantic torpedo, atlantic wolffish, barndoor skate, black sea bass, bluefish, bluntnose stingray, buckler dory, bullnose ray, clearnose skate, cownose ray, cusk, fawn cusk-eel, goosefish, greater amberjack, haddock, little skate, ocean pout, offshore hake, pollock, red hake, rosette skate, roughtail stingray, sand tiger, sandbar shark, sea raven, silver hake, smooth butterfly ray, smooth dogfish, smooth skate, southern stingray, spanish mackerel, spiny butterfly ray, spiny dogfish, spotted hake, striped bass, summer flounder, thorny skate, weakfish, white hake, windowpane flounder, winter flounder, winter skate, witch flounder and yellowtail flounder

Summary of Trends

Let’s start with these pieces, and we can then discuss doing something with the extremes of the size distribution–changes in small vs. large individuals, where we pre-define size bins as you’ve done in slides 7 and 8, or perhaps using an approach similar to Lora’s approach of defining the upper/lower percentiles of size. - KMillz

---

Part 2: Relationships to External Factors

The following plots and summaries will relate the Wigley species spectra to Temperature and Live Landings

# # Model Dataset for Wigley Species Subset
wigley_bmspectra_model_df <- read_csv(here::here("Data/model_ready/wigley_community_bmspectra_mod.csv"))

# Use one for plots
wigley_bmspectra_model_df <- wigley_bmspectra_model_df %>% 
  mutate(survey_area = case_when(
    survey_area == "GoM" ~ "Gulf of Maine",
    survey_area == "GB" ~ "Georges Bank",
    survey_area == "SNE" ~ "Southern New England",
    survey_area == "MAB" ~ "Mid-Atlantic Bight"),
  survey_area = factor(survey_area, levels = area_levels_long),
  season = fct_rev(season))

Bottom Temperature Changes

Bottom temperatures have increased in all four regions. The distribution of seasonal bottom temperatures are such that Gulf of Maine experiences a much smaller difference in seasonal temperature swings than the other regions.

temp1 <- wigley_bmspectra_model_df %>% 
  ggplot(aes(est_year, bot_temp, color = season)) +
  geom_point(size = 0.8, alpha = 0.5) +
  geom_ma(size = 1, n = 5, ma_fun = SMA, linetype = 1) +
  scale_color_gmri() + 
  facet_wrap(~survey_area, ncol = 1) +
  scale_y_continuous(labels = label_number(suffix = "\u00b0C")) +
  labs(y = "Bottom Temperature", x = "Year", color = "5-Year Smooth")


# Plot the distribution as a raincloud plot
temp2 <- wigley_bmspectra_model_df %>% 
  ggplot(aes(fct_rev(survey_area), bot_temp, color = season, fill = season)) +
   ggdist::stat_halfeye(
     alpha = 0.4,
     adjust = .5, 
     width = .6, 
    .width = 0, 
    justification = -.2, 
    point_colour = NA
  ) + 
  geom_boxplot(
    fill = "transparent",
    width = .15, 
    outlier.shape = NA
  ) +
  ## add dot plots from {ggdist} package
  ggdist::stat_dots(
    ## orientation to the left
    side = "left", size = 1,
    ## move geom to the left
    justification = 1.12, 
    ## adjust grouping (binning) of observations 
    binwidth = .25
  ) + 
  # geom_point(
  #   ## draw horizontal lines instead of points
  #   shape = 95,
  #   size = 10,
  #   alpha = .1) +
  scale_color_gmri() + 
  scale_fill_gmri() + 
  scale_y_continuous(labels = label_number(suffix = "\u00b0C")) +
  coord_flip() +
  theme(legend.position = "none") +
  labs(y = "Bottom Temperature", x = "", color = "Season")

temp1 | temp2

The overlap in bottom temperature distributions is very similar to the overlap in mass spectra exponents.

wigley_b_dist_plot <- wigley_bmspectra_model_df %>% 
  ggplot(aes(fct_rev(survey_area), b, color = season, fill = season)) +
  ggdist::stat_halfeye(
     alpha = 0.4,
     adjust = .5, 
     width = .6, 
    .width = 0, 
    justification = -.2, 
    point_colour = NA) + 
  geom_boxplot(
    fill = "transparent",
    width = .15, 
    outlier.shape = NA) +
  ## add dot plots from {ggdist} package
  ggdist::stat_dots(
    ## orientation to the left
    side = "left", size = 1,
    ## move geom to the left
    justification = 1.12, 
    ## adjust grouping (binning) of observations 
    binwidth = .005) + 
  scale_color_gmri() + 
  scale_fill_gmri() + 
  coord_flip() +
  labs(
    y = "ISD Exponent", 
    x = "",
    title = "Individual Body Mass Distribution (1g - Max)")



# Plot next to bottom temperatures
wigley_b_dist_plot | temp2

Which makes me lean towards thinking that the size distribution is responsive to the ambient water temperatures, and perhaps less stationary and responsive to long-term trends in some area.

wigley_bmspectra_model_df %>% 
  ggplot(aes(bot_temp, b)) +
  geom_point(aes(color = season, shape = survey_area)) +
  geom_smooth(method = "lm", color = "black") +
  scale_color_gmri() +
  scale_x_continuous(labels = label_number(suffix = deg_c)) +
  labs(title = "Broad Relationship to Ambient Bottom Temperature")

Total Landings

wigley_bmspectra_model_df %>% 
  distinct(est_year, survey_area, total_weight_lb) %>% 
  ggplot(aes(est_year, total_weight_lb/1e6)) +
  geom_col(alpha = 0.5) +
  geom_ma(aes(color = "5-Year Smooth"), size = 1, n = 5, ma_fun = SMA, linetype = 1) +
  scale_color_manual(values = "orange") +
  facet_wrap(~survey_area, ncol = 1) +
  scale_y_continuous(labels = label_number(suffix = "M")) +
  labs(y = "Total Landings (live lb.)", x = "Year", color = "Moving Average")

---

Driver Model

The following model has been used to determine the relationship between landings and seasonal bottom temperature on b.

lmer(
  b ~ survey_area*season*scale(roll_temp) + survey_area * scale(log(total_weight_lb)) + (1|est_year), 
  data = wtb_model_df)

#### Model 2: Temperature & Fishing ####


# Drop NA's
wtb_model_df <- drop_na(wigley_bmspectra_model_df, total_weight_lb, bot_temp, b) %>%
  rename(area = survey_area) %>% 
  left_join(area_df) %>% 
  # Do rolling BT within a season * region
  group_by(survey_area, season) %>%
  mutate(
    roll_temp = zoo::rollapply(bot_temp, 5, mean, na.rm = T, align = "right",  fill = NA)) %>% 
  ungroup() %>% 
  mutate(
    yr_num = as.numeric(est_year),
    yr_fac = factor(est_year),
    survey_area = factor(survey_area, levels = area_levels),
    season = factor(season, levels = c("Spring", "Fall")),
    landings = total_weight_lb,
    yr_seas = str_c(season, est_year))


# Make the model
mod2 <- lmer(
  b ~ survey_area*season*scale(roll_temp) + survey_area*scale(log(total_weight_lb)) + (1|est_year), 
  data = wtb_model_df)


# summary(mod2)
# check_model(mod2)

Drive Model Sumamry

mod2 %>% 
  gtsummary::tbl_regression()   %>% 
  add_global_p(keep = T) %>% 
  bold_p(t = 0.05)  %>%
  bold_labels() %>%
  italicize_levels() %>% 
  as_gt() %>% 
  tab_header(title = "Wigley Community - Bodymass Distribution & Drivers")

Wigley Community - Bodymass Distribution & Drivers
Characteristic	Beta	95% CI 1	p-value
survey_area			0.030
GoM	—	—
GB	-0.13	-0.29, 0.02	0.088
SNE	0.07	-0.06, 0.19	0.3
MAB	0.05	-0.05, 0.15	0.3
season			0.2
Spring	—	—
Fall	0.06	-0.04, 0.17	0.2
scale(roll_temp)	-0.01	-0.13, 0.10	0.8
scale(log(total_weight_lb))	0.08	0.00, 0.15	0.048
survey_area * season			<0.001
GB * Fall	0.17	0.00, 0.35	0.051
SNE * Fall	-0.12	-0.29, 0.06	0.2
MAB * Fall	-0.22	-0.39, -0.06	0.009
survey_area * scale(roll_temp)			0.007
GB * scale(roll_temp)	-0.24	-0.40, -0.08	0.004
SNE * scale(roll_temp)	0.02	-0.14, 0.18	0.8
MAB * scale(roll_temp)	-0.10	-0.27, 0.07	0.3
season * scale(roll_temp)			0.7
Fall * scale(roll_temp)	0.03	-0.11, 0.16	0.7
survey_area * scale(log(total_weight_lb))			0.4
GB * scale(log(total_weight_lb))	-0.06	-0.17, 0.06	0.3
SNE * scale(log(total_weight_lb))	-0.05	-0.17, 0.07	0.4
MAB * scale(log(total_weight_lb))	-0.07	-0.14, 0.01	0.094
survey_area * season * scale(roll_temp)			0.031
GB * Fall * scale(roll_temp)	0.16	-0.03, 0.36	0.10
SNE * Fall * scale(roll_temp)	-0.14	-0.33, 0.06	0.2
MAB * Fall * scale(roll_temp)	0.06	-0.13, 0.26	0.5
1 CI = Confidence Interval

Temperature Relationship

# Clean up the plot:
# Plot the predictions over data
mod2_preds <- as.data.frame(
  ggpredict(
    mod2, 
    terms = ~ roll_temp*survey_area*season))   %>% 
  mutate(
    survey_area = factor(group, levels = area_levels),
    season = factor(facet, levels = c("Spring", "Fall")))



#### Trend Posthoc  ####
trend_df <- emtrends(
  object = mod2,
  ~survey_area * season,
  var = "roll_temp")


# Drop effect fits that are non-significant  ###
mod2_emtrend <- emtrends(
  object = mod2,
  specs =  ~ survey_area*season,
  var = "roll_temp") %>%
  as_tibble() %>%
  mutate(
    zero = 0,
    non_zero = if_else(between(zero, lower.CL, upper.CL), F, T))




# Limit temperature plotting range
actual_range <- wtb_model_df %>% 
  group_by(season, survey_area) %>% 
  summarise(min_temp = min(bot_temp)-2,
            max_temp = max(bot_temp)+2,
            .groups = "drop")



# Plot over observed data
mod2_preds %>% 
  left_join(actual_range) %>% 
  filter((x < min_temp) == F,
         (x > max_temp) == F) %>% 
  left_join(select(mod2_emtrend, survey_area, season, non_zero)) %>%
  filter(non_zero) %>%
  ggplot() +
  geom_ribbon(aes(x, ymin = conf.low, ymax = conf.high, group = survey_area), alpha = 0.1) +
  geom_line(
    aes(x, predicted, color = season), 
    linewidth = 1) +
  geom_point(
    data = wtb_model_df,
    aes(roll_temp, b, color = season),
    alpha = 0.4,
    size = 1) +
  facet_grid(survey_area~., scales = "free") +
  scale_color_gmri() +
  scale_x_continuous(labels = label_number(suffix = deg_c)) +
  labs(y = "Exponent of ISD",
       title = "Wigley Species, Body Mass Spectra",
       x = "5-Year Rolling Average Bottom Temperature")

Landings Relationship

# Clean up the plot:
# Plot the predictions over data
mod2_preds <- as.data.frame(
  ggpredict(
    mod2, 
    terms = ~ total_weight_lb * survey_area * season))   %>% 
  mutate(
    survey_area = factor(group, levels = area_levels),
    season = factor(facet, levels = c("Spring", "Fall")))



#### Trend Posthoc  ####
trend_df <- emtrends(
  object = mod2,
  ~survey_area * season,
  var = "total_weight_lb")
#summary(trend_df, infer= c(T,T))


# Drop effect fits that are non-significant  ###
mod2_emtrend <- emtrends(
  object = mod2,
  specs =  ~ survey_area*season,
  var = "log(total_weight_lb)") %>%
  as_tibble() %>%
  mutate(
    zero = 0,
    non_zero = if_else(between(zero, lower.CL, upper.CL), F, T),
    group = str_c(survey_area, "X", season))




# Plot over observed data
mod2_preds %>% 
  left_join(select(mod2_emtrend, survey_area, season, non_zero)) %>%
  filter(non_zero) %>%
  ggplot() +
  geom_ribbon(aes(x, ymin = conf.low, ymax = conf.high, group = season), alpha = 0.1) +
  geom_line(
    aes(x, predicted, color = season), 
    linewidth = 1) +
  geom_point(
    data = wtb_model_df,
    aes(total_weight_lb, b, color = season),
    alpha = 0.4,
    size = 1) +
  facet_grid(survey_area~., scales = "free") +
  scale_color_gmri() +
  scale_x_continuous(transform = "log10", labels = label_log(base = 10)) +
  labs(y = "Body Mass Spectra Slope (b)",
       title = "Wigley Species, Body Mass Spectra",
       x = "Total Landings (lb.)")

Side Stories

Everything below is digging into different nuances of the approach or the community composition. These things are of secondary importance and will be moved out.

Spiny Dogfish Sensitivity

Spiny dogfish constitute a large majority fraction of biomass in the MAB during the Spring survey. Their numbers have also been increasing over time. This acts to shallow the spectra as they are one of the larger species routinely sampled by the trawl survey.

If we remove them from the estimation and re-run the slopes this is what changes.

How much can one species shift the spectra, and is this a definitive/conclusive proof that its the dogfish shallowing the slope in MAB?

# # Run MAB with and without spiny dogfish
# # Run the year, season, area fits for the filtered data
# no_dogs_bmspectra <- group_binspecies_spectra(
#   ss_input = filter(wigley_in, comname != "spiny dogfish"),
#   grouping_vars = c("est_year", "season", "survey_area"),
#   abundance_vals = "numlen_adj",
#   length_vals = "length_cm",
#   use_weight = TRUE,
#   isd_xmin = 1,
#   global_min = TRUE,
#   isd_xmax = NULL,
#   global_max = FALSE,
#   bin_width = 1,
#   vdiff = 2)

# # Save those
# write_csv(
#   no_dogs_bmspectra,
#   here::here("Data/processed/wigley_species_nodogfish_bmspectra.csv"))

# Read them in, do some plotting
no_dogs_bmspectra <- read_csv(
  here::here("Data/processed/wigley_species_nodogfish_bmspectra.csv")) %>% 
  mutate(survey_area = factor(survey_area, levels = area_levels))


# Format a little
no_dogs_bmspectra <- no_dogs_bmspectra %>% 
  mutate(est_year = as.numeric(est_year))


# Join them together
dfish_results <- bind_rows(
   list(
     "Wigley Full" = wigley_bmspectra,
     "Wigley w/o Spiny Dogfish" =  no_dogs_bmspectra), 
   .id = "community") %>% 
  mutate(metric = "bodymass_spectra") %>% 
  mutate(
    survey_area = fct_relevel(survey_area, area_levels))




# Get trends for no dogfish
nodfish_mod <- lmer(
  formula = b ~ est_year * survey_area * season + (1|est_year),
  data = no_dogs_bmspectra)



dogfish_sens_predictions <- bind_rows(list(
  "bodymass_spectra-Wigley Full" = get_preds_and_trendsignif(bmspectra_mod_wig),
  "bodymass_spectra-Wigley w/o Spiny Dogfish" = get_preds_and_trendsignif(nodfish_mod)), 
  .id = "model_id") %>% 
  separate(model_id, into = c("metric", "community"), sep = "-") %>% 
  mutate(metric = fct_rev(metric))



# Plot the differences
ggplot() +
  geom_ribbon(
    data = filter(dogfish_sens_predictions, non_zero == TRUE),
    aes(x, ymin = conf.low, ymax = conf.high, fill = season),
    linewidth = 1, alpha = 0.35) +
  geom_point(
    data = dfish_results,
    aes(est_year, b, color = season),
    size = 0.8, alpha = 0.5) +
  geom_line(
    data = filter(dogfish_sens_predictions, non_zero == TRUE),
    aes(x, predicted, color = season),
    linewidth = 1, alpha = 1) +
  scale_fill_gmri() +
  scale_color_gmri() +
  facet_grid(survey_area ~ metric*community, scales = "free") +
  labs(
    x = "Year",
    y = "Exponent of Size Spectra",
    title = "Shift in Spectra Trend(s) with Exclusion of Spiny Dogfish")

Story Thoughts

Below is a table of some papers operating in the same area or around the same topic that are particularly relevant:

shelf_papers <- tribble(
  ~"Author", ~"Year", ~"Conjecture",
  "Friedland et al.", 2024, "Body size has declined despite more abundance & biomass. Competition is preventing larger sizes.",
  "Krumsick", 2020, "Empirical slopes from trawl survey in labrador sea were steeper than theoretical from nitrogen stable isotope ratios"
)


shelf_papers %>% gt()

Author	Year	Conjecture
Friedland et al.	2024	Body size has declined despite more abundance & biomass. Competition is preventing larger sizes.
Krumsick	2020	Empirical slopes from trawl survey in labrador sea were steeper than theoretical from nitrogen stable isotope ratios

There are a couple core ideas about what changes have occurred or have been ocurring over the Northeast Shelf with relevance to the community that is sampled by the trawl survey:

---